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Development of a Fully Automated Desktop Analyzer and Ultrahigh Sensitivity Digital Immunoassay for SARS-CoV-2 Nucleocapsid Antigen Detection.
Chiba, Ryotaro; Miyakawa, Kei; Aoki, Kotaro; Morikawa, Takamitsu J; Moriizumi, Yoshiki; Degawa, Takuma; Arai, Yoshiyuki; Segawa, Osamu; Tanaka, Kengo; Tajima, Hideji; Arai, Susumu; Yoshinaga, Hisatoshi; Tsukada, Ryohei; Tani, Akira; Fuji, Haruhito; Sato, Akinobu; Ishii, Yoshikazu; Tateda, Kazuhiro; Ryo, Akihide; Yoshimura, Toru.
Afiliação
  • Chiba R; Research and Development, Abbott Japan LLC, Matsudo 270-2214, Japan.
  • Miyakawa K; Department of Microbiology, Yokohama City University School of Medicine, Yokohama 236-0004, Japan.
  • Aoki K; Department of Microbiology and Infectious Diseases, Toho University School of Medicine, Tokyo 143-8540, Japan.
  • Morikawa TJ; Research and Development, Abbott Japan LLC, Matsudo 270-2214, Japan.
  • Moriizumi Y; Research and Development, Abbott Japan LLC, Matsudo 270-2214, Japan.
  • Degawa T; Research and Development, Abbott Japan LLC, Matsudo 270-2214, Japan.
  • Arai Y; Research and Development, Abbott Japan LLC, Matsudo 270-2214, Japan.
  • Segawa O; Precision System Science Co., Ltd., Matsudo 271-0064, Japan.
  • Tanaka K; Precision System Science Co., Ltd., Matsudo 271-0064, Japan.
  • Tajima H; Precision System Science Co., Ltd., Matsudo 271-0064, Japan.
  • Arai S; Sumitomo Bakelite Co., Ltd., Tokyo 140-0002, Japan.
  • Yoshinaga H; Sumitomo Bakelite Co., Ltd., Tokyo 140-0002, Japan.
  • Tsukada R; Sumitomo Bakelite Co., Ltd., Tokyo 140-0002, Japan.
  • Tani A; Olympus Corporation, Hachioji 192-8507, Japan.
  • Fuji H; Olympus Corporation, Hachioji 192-8507, Japan.
  • Sato A; Olympus Corporation, Hachioji 192-8507, Japan.
  • Ishii Y; Department of Microbiology and Infectious Diseases, Toho University School of Medicine, Tokyo 143-8540, Japan.
  • Tateda K; Department of Microbiology and Infectious Diseases, Toho University School of Medicine, Tokyo 143-8540, Japan.
  • Ryo A; Department of Microbiology, Yokohama City University School of Medicine, Yokohama 236-0004, Japan.
  • Yoshimura T; Research and Development, Abbott Japan LLC, Matsudo 270-2214, Japan.
Biomedicines ; 10(9)2022 Sep 15.
Article em En | MEDLINE | ID: mdl-36140390
ABSTRACT

BACKGROUND:

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak has had a significant impact on public health and the global economy. Several diagnostic tools are available for the detection of infectious diseases, with reverse transcription-polymerase chain reaction (RT-PCR) testing specifically recommended for viral RNA detection. However, this diagnostic method is costly, complex, and time-consuming. Although it does not have sufficient sensitivity, antigen detection by an immunoassay is an inexpensive and simpler alternative to RT-PCR. Here, we developed an ultrahigh sensitivity digital immunoassay (d-IA) for detecting SARS-CoV-2 nucleocapsid (N) protein as antigens using a fully automated desktop analyzer based on a digital enzyme-linked immunosorbent assay.

METHODS:

We developed a fully automated d-IA desktop analyzer and measured the viral N protein as an antigen in nasopharyngeal (NP) swabs from patients with coronavirus disease. We studied nasopharyngeal swabs of 159 and 88 patients who were RT-PCR-negative and RT-PCR-positive, respectively.

RESULTS:

The limit of detection of SARS-CoV-2 d-IA was 0.0043 pg/mL of N protein. The cutoff value was 0.029 pg/mL, with a negative RT-PCR distribution. The sensitivity of RT-PCR-positive specimens was estimated to be 94.3% (83/88). The assay time was 28 min.

CONCLUSIONS:

Our d-IA system, which includes a novel fully automated desktop analyzer, enabled detection of the SARS-CoV-2 N-protein with a comparable sensitivity to RT-PCR within 30 min. Thus, d-IA shows potential for SARS-CoV-2 detection across multiple diagnostic centers including small clinics, hospitals, airport quarantines, and clinical laboratories.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article